Primary Supervisor: Nicholas Warren (Sheffield)

Secondary Supervisor: Vanessa Hearnden and Zoltan Kis (Sheffield)

Research challenges addressed in this project:

Developing advanced nanoparticles is crucial for delivering sensitive pharmaceutical payloads such as proteins and nucleic acids. Such nanomedicines include new-generation vaccines which rely on lipid nanoparticles (LNPs), which are a complex mixture of components required to efficiently encapsulate and deliver the payload. However, there is huge potential for nanomedicines in a number of areas, but optimising these formulations is slow and significant challenges remain, including poor stability, high immunogenicity, and inefficient organ targeting.

To rapidly discover new formulations, the project will develop and implement a "self-driving laboratory" (SDL). This cyber-physical system, guided by machine learning, will radically accelerate the screening and optimisation of new components, such as novel ionisable lipids and functional polymers. The platform will integrate online analysis with offline and in vitro cell testing. The data collected will be fed to a Bayesian optimisation framework to efficiently identify formulations with the best balance of performance, cost, and quality, while also building a deeper mechanistic understanding of how hybrid formulations influence biological outcomes. The project suits an individual with a background in (bio)chemical science or engineering with an interest in learning about the implementation of digital tools for accelerating R&D.

Primary Supervisor: Clare Hoskins (Strathclyde)

Secondary Supervisor: Iain Oswald and Tell Tuttle (Strathclyde)

Research challenges addressed in this project:

Solid lipid nanoparticles (SLNs) are a class of nanocarriers composed of biocompatible lipids that solidify at room and body temperature. These lipids self-assemble into a densely packed crystalline core, which is stabilised by a surrounding layer of surfactants. This structural arrangement provides a versatile platform for the encapsulation of hydrophobic drug molecules. During the rapid crystallisation process, imperfections and deformations form within the lipid matrix, creating spaces where drug molecules can be incorporated. These imperfections are crucial for drug loading, but they also introduce variability in drug release profiles.

One of the key challenges in SLN formulation is the polymorphic transformation of the lipid core. Initially, the lipids tend to crystallise into a metastable alpha polymorphic form. However, over time or under specific environmental conditions—such as elevated temperature, humidity, or mechanical stress—this alpha form gradually transitions into the more thermodynamically stable beta polymorph. This transformation alters the internal structure of the SLNs, potentially leading to the expulsion of drug molecules from the lipid matrix or, conversely, tighter entrapment of the drug within the crystalline lattice. Both outcomes can significantly influence the release kinetics of the encapsulated drug, thereby affecting its bioavailability and therapeutic efficacy.
 
This project aims to systematically investigate the polymorphic transitions in SLNs and their impact on drug release behaviour and to use this data to develop a predictive model that correlates lipid polymorphism with drug release profiles and overall formulation stability. 

Primary Supervisor: Yvonne Perrie (Strathclyde)

Secondary Supervisor: Alistair Florence (Strathclyde)

Research challenges addressed in this project:

More info coming soon.

Primary Supervisor: Yvonne Perrie (Strathclyde)

Secondary Supervisors: Blair Johnston and Clare Hoskins (Strathclyde)

Research challenges addressed in this project:

More info coming soon.

Primary Supervisor:  Jan Sefcik (Strathclyde)

Secondary Supervisors: Clare Hoskins (Strathclyde)

Research challenges addressed in this project: 

More info coming soon.